Deep learning in 3D point cloud analysis has received increasing attention with the rising trend of Virtual Reality and 3D scene understanding applications, etc. Existing approaches have made great progresses in tasks such as point cloud classification [1]} and point cloud semantic segmentation [2]}, [3]}. One fundamental issue to be tackled with in point cloud analysis is the representation of unstructured point clouds. Some early methods discretize point clouds into regular volumetric grids which can be directly fed into standard 3D CNNs. However, two main problems coupled with this volumetric representation are information loss and huge memory consumption. A high resolution voxel grid leads to expensive computation cost, while a low resolution inevitably suffers from information loss during voxelization procedure.
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